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Dynamic data encryption with polarized feedback
Get PDFIn the ever-evolving domain of cryptography, achieving dynamic data encryption robustness remains paramount. This research paper delves into an innovative approach termed 'Dynamic Data Encryption with Polarized Feedback'. Leveraging neural network architectures, the study attempts to mimic the AES encryption and decryption process. After training the model for one million iterations using a singular AES key and cipher, the model achieved maximum accuracy when performing both encryption and decryption on its own. Intriguingly, when pitting the neural model against the traditional AES—encrypting with one and decrypting with the other—the success rate dropped significantly, achieving only 26 successful results out of 5,000 tests. This emphasizes the challenge in aligning neural encryption methods perfectly with conventional encryption techniques. The paper's code showcases an elegant interplay of PyTorch, the AES cipher, and a feedback mechanism that guides model retraining based on performance. The findings shed light on the complexities and potential pathways in neural cryptography
Comparing Machine Learning Models For Predicting Fuel Consumption In Energy Generation For The Food Processing Industry In Nigeria
Get PDFAccurate prediction of fuel consumption is crucial for effective energy management, cost optimization and environmental pollution management in the food processing industry in Nigeria. Machine learning models have shown promise in various domains for predicting and optimizing fuel and energy consumption. This research under the CRISP-DM methodology, compared the performance of three machine learning models which include Artificial Neural Networks, Support Vector Machines and Random Forest in predicting fuel consumption in energy generation in a food processing industry in Nigeria.
This research was conducted using historical data on fuel consumption and independent variables such as energy generated, gas consumption, gas pressure, etc. collected over 2 years from Flour Mills of Nigeria Plc.
The performance of the predicted models was evaluated based on Root Mean Square Error (RMSE), Mean Square Error (MSE) and R Square evaluation metrics. The Random Forest Model performed best across most metrics, with the lowest RMSE (8124.62 & 26061.43) and MSE (6.6E+07 & 6.8E+08) on both training and testing data, and a relatively high R-squared value (0.93 & 0.52). The ANN Model performed reasonably well, but the SVM Model had a comparatively poorer performance
Forecasting inflation rates in Turkey with Linear Regression, SARIMA, and LSTM
No full textTurkey, categorized under emerging economies, has seen fluctuations in its inflation rates, which have recently been notably high. This prompts the need for robust estimation models that can accurately predict inflationary trends. This study aims to contribute to the existing literature by testing and comparing three distinct inflation forecasting models; multilinear regression, SARIMA, and Long Short-Term Memory (LSTM) in the context of Turkey between 2004 and 2023. By contrasting these models with the CBRT's median market participant survey, and using Root Mean Square Error (RMSE) for model evaluation, the study seeks to identify the most accurate model for predicting inflation in Turkey. The outputs of these study shows that usage of SARIMA and LSTM models together outperforms than the individual models and the benchmark survey. Individual level, SARIMA were performed better to capture extreme fluctuations in time series than others
An investigation into the relationship between diversity and compliance in Irish organisations
Get PDFThe aim of this research was to investigate the perceptions of senior compliance professionals in Irish organisations regarding potential connections between the area of diversity and compliance. The research followed a qualitative design to address the research questions and to gather and summarise the data. Semi-structured interviews were used to gather primary data from senior compliance professionals based in five organisations spanning the public sector and the financial services sector. The Central Bank of Ireland(‘Central Bank’) report Behaviour and Culture of the Irish Retail Banks (2018a) was reviewed alongside Irish and UK Corporate Governance Codes and Corporate Governance Reviewsin the UK. The Central Bank in their reviews of retail banking following the financial crises of 2007 - 2008, have suggested that increasing diversity on boards is related to an improvement in culture and behaviours. The variables in the research are diversity in the form of gender and compliance in public sector organisations as opposed to private financial services companies. The following themes emerged from the results found. Diversity is not a topic in Compliance Programmes and companies are largely not making the link with improvements in diversity having a positive effect on compliance. However, in assessing future risks, financial services companies are aware of the changing regulatory environment and are taking account of diversity in the Compliance Programmes. Analysis of the interviews suggested that a collaborative type of leadership model is viewed as conducive to an inclusive culture where diversity can be embedded. The future focus now needs to move from not alone having diversity in the workplace but to the proactive inclusion of those diverse people in the organisation
A qualitative analysis on the client's experience of the propeller model approach to counselling therapy --
No full textThe successful development of psychotherapeutic and counselling approaches can reflect the evolution and innovation within the industry of psychological treatment. Measuring the benefits of an approach involves inquiry into how it is conducted, the practitioner conducting it, and even the setting in which it is conducted. The aims of this exploratory study are to discover if a psychotherapeutic approach, the Propeller Model Approach, serves its purpose of increasing self-awareness for clients. Such awareness is distinguished in the study as intrinsic and instrumental and aims to build on the theoretical framework that self-awareness is beneficial to the human condition. In order to conduct the study, a semi-structured interview was conducted with five clients engaged in counselling therapy after an individual online counselling session. Each session utilized the approach implemented by the practitioner for the study who was also the principal researcher. To understand the nature of the participant experience of the approach, a phenomenological interpretivist epistemology was adhered to. The use of abduction assisted in connecting the ontological construction of participant feedback from their interaction with the approach to new potential hypotheses. The data of the feedback was analysed through a reflexive thematic analysis that respected the hermeneutical nature of the coding and thematization of the data. The study serves as an example of evidence-based research into a new psychotherapeutic approach and can guide practitioners interested in utilizing the Propeller Model Approach to counselling therapy or for general exploration into human identity
Comparative Study Between Deep Learning and Traditional Machine Learning Models for Sentiment Analysis
No full textSentiment Analysis represents a dynamic field of research within the field of text mining. It involves the computational analysis of opinions, emotions, and subjective elements present in written text. This process entails the systematic examination of sentiments conveyed through language. The study investigates the relative effectiveness of four sentiment analysis techniques: (1) traditional supervised machine learning model using logistic regression, (2) Naive Bayes,(3) Support Vector Machine, and (4) Advanced supervised deep learning model using Bidirectional Encoder Representations from Transformers (BERT). A thorough examination was conducted on a publicly accessible dataset containing 10,261 Amazon reviews focusing on musical instruments. These reviews capture customer emotions, closely linked with associated ratings. Due to its current relevance, precise interpretation of sentence context and determination of whether it expresses positive or negative sentiment is of paramount importance. The efficacy of sentiment classification evaluation was enhanced by analysing metrics including accuracy, precision, recall, and the F1 score
“But it’s just one short video” … Is TikTok impacting sustained attention or working memory?
No full textThe current study aimed to investigate the impact both short-term and long-term TikTok exposure had on an individual’s sustained attention and working memory capacity (WMC). A true-experimental design was employed in a pre-post video design whereby 75 participants (m= 28%; f=69.3%) were spilt into a control group and experimental group. Both groups completed one round of a sustained attention task and WMC test, before watching a video and then proceeding on to complete a second round of both tasks. The experimental group was exposed to a TikTok video compilation and the control group was exposed to a low-stimulation video. No between or within group differences were found indicating immediate TikTok exposure did not impact attention or memory. A significant correlation was found between general TikTok use and recall ability, suggesting that long-term TikTok exposure may hinder WMC. Females were found to have significantly higher TikTok use than males
Can Irish Public Libraries Fulfil their Role as Places of Sanctuary within Contemporary Society?
No full textThis study explores, from the perspective of librarians working in Irish Public Libraries, whether Irish Public Libraries can fulfil their role as places of sanctuary within contemporary society as they are currently structured, resourced and provided for, what these librarians perceive this concept, role and its requirements to be and how they define and delimit this role. Mixed methods in the form of questionnaire and interview were applied. Participants from 14 counties in Ireland ranging from Library Assistant to County Librarian included 71 librarians by questionnaire and 6 librarians by interview. Research findings show that the concept and role of Irish Public Libraires as places of sanctuary are perceived by librarians as wide ranging and without clear boundaries and that librarians perceive the fulfilment of this role as achievable with government support that is informed by librarians
Storytelling in Augmented Reality: Challenges And Possibilities For Immersive Digital Marketing
Get PDFIn Marketing, storytelling is the ability to convey content through a well-crafted plot and engaging narrative. As a marketing strategy, storytelling works under the assumption that people retain information more effectively when it is conveyed in the form of a narrative, rather than a mere list of facts. Augmented Reality (AR) disrupts narrative perspectives, shifting stories from third-person to first-person experiences. In this context, this mixed-method research investigates optimal AR storytelling practices in this technology’s evolving landscape. It delves into how AR attributes can enrich narratives, delivering engaging, memorable user experiences. Interviews with AR experts unravel effective AR Storytelling, while a consumer survey gathered opinions on AR storytelling. Results underline the significance of user context and participation, shaping successful experiences. Efficient immersive Digital Marketing involves structured audience targeting and consistent performance evaluation via engagement metrics. In this evolving landscape, further research is necessary regarding the domain of ubiquitous AR
Secure data transmission using cryptography, image processing and steganography
No full textIn today’s digital era data plays a crucial role, users are passing the information from one end to another without ensuring the security of the data. It is paramount to ensure the confidentiality, integrity and security of the data while it is being transmitted over any of the platforms. Many platforms use various types of security algorithms like cryptography, steganography but it is found that is not enough to make the secure data transfer. Attackers are always two steps ahead and find one or another way to violate the data security. Hence this report presents a comprehensive approach that combine cryptography using AES algorithm with QR code generation and least significant bit Steganography to achieve a robust framework and enhanced data security. This enhanced data security methodology addresses the challenges of secure data transmission across digital platforms.
The foundation of proposed methodology lies in the implementation of AES cryptography, which is widely recognized and utilized robust secure cryptography algorithm. AES is employed to transform plain text secret message into cipher text using encryption operation. This encryption ensures the confidentiality and protection from unauthorized access of data. The further implementation of Quick Response code enhances the secure data transmission and hiding data into an image format. This QR codes serve as bridge between digital and physical mediums and enables the sufficient data exchange while maintaining the encryption integrity.
To add one more level of security with AES and QR, LSB steganography is also added. In this layer of security LSB uses least significant bit of cover image pixels data and subtly embed the encrypted data into it. This covert channel not only complements the encryption but also adds the level of obscurity. This makes challenging for unauthorized users to detect the presence of the information. Proposed methodology is evaluated through rigorous testing to assess its security, effectiveness and its efficiency in real world scenarios. The experimental results and demonstrate the successful integration of AES cryptography, QR code operations and LSB steganography. This research contributes to the advancement of secure data transmission techniques